Analysing consumer sentiments in product reviews using full stack web development

Abstract

Analysing consumer sentiment in product reviews is an effective approach to understand customer opinions in modern ecommerce platforms. With the rapid increase in online shopping, massive volumes of textual reviews are generated, making manual analysis inefficient and impractical. This paper presents a fullstack web-based sentiment analysis system that automatically classifies product reviews into positive, negative, and neutral sentiments using Natural Language Processing (NLP) and Machine Learning techniques. The proposed system performs text preprocessing, TF-IDF feature extraction, and sentiment classification using supervised learning algorithms such as Logistic Regression. The system is implemented using Python, Django, and Scikit-learn, providing real-time sentiment analysis, visualization, and user-friendly interaction for effective decision-making.